1. Field of the Invention
The present invention relates to an electronic apparatus capable of recommending, among video contents shot by a user, a video content that a user might want to view, so as to reproduce the video content, and to a content recommendation method and a program in the electronic apparatus.
2. Description of the Related Art
In the related art, a technique of recommending a video content (hereinafter, abbreviated as video) that a viewer of a certain video supposedly wants to view next has been used in “YouTube” (URL: http://www.youtube.com), for example.
In the recommendation technique, information items of “tag information”, “evaluation of viewer”, and “action of viewer” indicating a title, a tag, and a description which are associated with a video have been used. The “tag information” includes information items that are considered by a creator of a video to be associated with the video, and those information items need to be input by the creator. The degree of freedom of the tag information as information is high, but the tag information depends on subjective view of the creator. Specifically, one thinks that videos are similar to each other, the other may not think so. The “evaluation of viewer” includes evaluation information items of videos by viewers. For example, the evaluation includes good, middle, and bad evaluations. Those information items need to be input by the viewers. The “action of viewer” includes information items regarding the fact that an image has been reproduced to the end, the fact that a video has been shifted to another video in the middle, and which video is viewed before another video is viewed, and those information items need to be input by viewers.
The recommendation technique is mainly divided into a “rule-based” system, a “content-based” system, and a “collaborative filtering” system.
The “rule-based” system means a technique in which a recommendation rule is predetermined, for example, “in a case of A, B is recommended”. This technique uses the “tag information”. Therefore, as information items to be used are increased, the maintenance and inputs for rule setting become troublesome. Further, in a case where no tag information can be obtained, it does not work well.
The “content-based” system means a technique of measuring similarity between videos, and recommending a similar video. In this technique, the “tag information” is used. Therefore, if tag information items between videos are not different from each other contents similar to each other may be recommended. Further, in a case where no tag information item can be obtained, it does not work well.
The “collaborative filtering” system means a technique of calculating similarity of preferences between users based on information items of “evaluation of viewer”, “action history of viewer”, and the like irrespective of the contents of the videos, and recommending a video, which the user has not viewed yet, taking a user having a high similarity in preference with respect to a target user for reference. Further, other than the user-based system of calculating the similarity of preferences between users, there has also been a content-based system of calculating the similarity between videos based on “evaluation of viewer” or “action history of viewer”. Both methods as described above perform a recommendation based on part not associated with the contents of videos, and hence there is a possibility that surprising contents are recommended. In addition, in the above-mentioned methods, it is difficult to obtain a sufficient result if histories of users are not sufficiently accumulated.